A Cluster-Based Classifier Ensemble as an Alternative to the Nearest Neighbor Ensemble

The combination of multiple classifiers, commonly referred to as an ensemble, has previously demonstrated the ability to improve overall classification accuracy in many application domains. Some ensemble techniques, however, cannot easily improve the performance of stable classification methods. One such example of a stable classification method is the k Nearest Neighbor (kNN) Classifier. In this paper we propose an alternative to the kNN ensemble method through the use of a clustering technique applied for the purpose of selecting the neighborhood of a new instance. In addition, a novel combination function based on exponential support (ExSupp) has been introduced. The proposed approach exhibited improved classification results in 16 out 20 data sets which were considered in comparison with a single kNN and a kNN ensemble based approach. Besides higher classification accuracy the proposed method exhibited higher levels of efficiency in terms of classification time.